Holistic Graph-based Motion Prediction

Grimm, Daniela; Schörner, Philip; Dreßler, Moritz; Zöllner, J. Marius

doi:10.1109/icra48891.2023.10161468

Cited by 5 publications

(2 citation statements)

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“…Subsequently, this edge feature is concatenated with the source node feature to generate a new node feature used for calculating the attention coefficient and subsequent aggregation. Grimm et al [29] use a holistic graph-based method combining time variant information in a single graph, instead of using separate information for each time-step like [30], [31]. This approach is among the first to model the entire encoding step in a single graph for trajectory prediction.…”

Section: Heterogeneous Graph-based Representationmentioning

confidence: 99%

“…HEAT [18] represents interactions as an edgefeatured heterogeneous graph but still ignores the semantic relationships among the agents. In [29], Grimm et al uses the edge type attent to represent the interaction between agents. In [19], [32], semantic information regarding agent interaction is represented by relation types longitudinal, lateral, and intersecting, ignoring relation with pedestrian.…”

Section: Agent Interaction Modelingmentioning

confidence: 99%

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A novel method for drug-target interaction prediction based on graph transformers model

Wang

Hong-mei

Guo

et al. 2022

Preprint

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Background:drug-target interactions prediction(DTIs) becomes more and moreimportant for accelerating drug research and drug repositioning. drug-targetinteraction network is a typical model for DTIs prediction. As many differenttypes of relationships exist between drug and target, drug-target interactionnetwork can be used for modeling drug-target interaction relationship. Recentworks on drug-target interaction network are mostly concentrate on drug node ortarget node and neglecting the relationships between drug-target. Results:We propose a novel prediction method for modeling the relationshipbetween drug and target independently. Firstly, we use different level relationshipsof drugs and targets to construct feature of drug-target interaction. Then, we useline graph to model drug-target interaction. After that, we introduce graphtransformer network to predict drug-target interaction. Conclusions:We introduce line graph to model the relationship between drug andtarget. After transformed drug-target interaction from links into nodes, we usegraph transformer network to fulfill drug-target interaction prediction task. Keywords: drug-target interaction; graph attention network; line graph

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Section: Heterogeneous Graph-based Representationmentioning

confidence: 99%